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In forestry breeding programmes, evaluating the genetic merit of tree genotypes involves navigating multiple sources of variability. Among these, GxE stands out as a crucial factor to consider. GxE describes changes in the ranking of genotypes across different environments and understanding it can have significant implications in optimizing experimental design, resources, and decision-making in breeding programmes.
What is GxE?
GxE reflects how genotypes respond differently to varying environmental conditions. This variability is only evident when genotypes are tested across multiple locations with diverse environmental conditions. For example, a tree genotype that thrives in one site might perform poorly in another due to differences in soil type, climate, or other environmental factors.
Designing Experiments to Evaluate GxE
Here are some key considerations when assessing GxE:
- Connectivity Between Sites:
For accurate GxE evaluation, genotypes (e.g., families or clones) should ideally be represented across all test sites. While perfect representation is rarely achievable, a general rule is to aim for at least 40% connectivity. Note that this means that 40% of genotypes should be in common between each pair of sites, but not necessarily the same 40% of genotypes across all sites.
Advances in pedigree data and genomic tools allow for greater flexibility in connectivity requirements. For example, deeper pedigrees (three or more generations) or genomic data can provide robust insights even with reduced direct connectivity. For example, cousins are also a form of connectivity. However, these scenarios should be evaluated on a case-by-case basis.
- Balancing Resources:
The magnitude of GxE is critical. When this source of variation is large, especially with respect to the error variance, it is important to make more efforts to evaluate it carefully. For example, given the same amount of resources, it is sensible to privilege using more sites in an experiment when the GxE is large, at the expense of the number of replications per site.
The Role of Genomic Data in GxE Evaluation
The integration of genomic data has shifted the approach to GxE evaluation. This is because, with this kind of data, we are not looking at a good connectivity of genotypes across sites anymore, but rather at a good representation of every allele in each site and across sites.
Forestry experimental trials may need years (or decades!) before being prime for phenotypic evaluation. Hence, in this case, obtaining genomic data on poorly connected existing trials can be a solution to increase estimation accuracy of all genetic effects, including GxE.
Read more about experimental design for single-tree vs multi-tree plots.
Conclusion
Addressing GxE is not just a theoretical exercise: it has real-world implications for breeding programmes. By designing experiments that account for GxE, forestry (and other plant) researchers can identify stable genotypes that perform consistently across diverse conditions, ensuring better adaptability and reliability on their performance. Alternatively, genotypes performing particularly well in certain conditions may also be selected for specific environments. Understanding GxE also allows you to optimize your resource allocation effectively and manage the trade-off between the number of sites and the number of replicates per site.
At VSNi, we understand the complexities of estimating GxE and offer both the analytical tools (ASReml) and the consultancy services to help researchers navigate these challenges. Whether you need guidance on experimental design, connectivity assessment, or integrating pedigree and genomic data, our experts are here to support your breeding goals. Let us help you harness the full potential of your data and advance your forestry breeding program to new heights.
